With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impu...With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.展开更多
Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.T...Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.展开更多
本文基于超效率数据包络分析(SE-DEA:Super Efficiency Data Envelopment Analysis)对城市交通行业的运营面板数据进行分析,利用Gini系数提高了SE-DEA的判别能力,得到了综合效率值,降低了传统DEA效率易受指标维数影响的缺陷,避免了同时...本文基于超效率数据包络分析(SE-DEA:Super Efficiency Data Envelopment Analysis)对城市交通行业的运营面板数据进行分析,利用Gini系数提高了SE-DEA的判别能力,得到了综合效率值,降低了传统DEA效率易受指标维数影响的缺陷,避免了同时出现多个有效评价结果,而且可以保证该方法总有可行解,在提升DEA方法判别能力的同时有效降低主观因素对评价结果的影响。通过分析全国31个城市2006年到2012年的公共交通面板数据,利用SE-DEA-Gini方法对公共交通行业进行客观的绩效评价,得到了全面评价城市公共交通的综合效率,在此结果上提出了可行的建议,指出了各城市公共交通发展中存在的问题,为保证优先发展城市公共交通战略的顺利实施提供了决策依据。展开更多
基金the Science and Technology Commission of Shanghai Municipality(No.19030501100)the Technical Service Platform for Vibration and Noise Testing and Control of New Energy Vehicles(No.18DZ2295900)。
文摘With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.
基金supported by the National Natural Science Foundation of China under Grant No.62461041Natural Science Foundation of Jiangxi Province under Grant No.20224BAB212016 and No.20242BA B25068China Scholarship Council under Grant No.202106825021.
文摘Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.
文摘本文基于超效率数据包络分析(SE-DEA:Super Efficiency Data Envelopment Analysis)对城市交通行业的运营面板数据进行分析,利用Gini系数提高了SE-DEA的判别能力,得到了综合效率值,降低了传统DEA效率易受指标维数影响的缺陷,避免了同时出现多个有效评价结果,而且可以保证该方法总有可行解,在提升DEA方法判别能力的同时有效降低主观因素对评价结果的影响。通过分析全国31个城市2006年到2012年的公共交通面板数据,利用SE-DEA-Gini方法对公共交通行业进行客观的绩效评价,得到了全面评价城市公共交通的综合效率,在此结果上提出了可行的建议,指出了各城市公共交通发展中存在的问题,为保证优先发展城市公共交通战略的顺利实施提供了决策依据。
